Method and apparatus for converting two-dimensional image into three-dimensional image

ABSTRACT

The present disclosure provides a method for converting a Two-Dimensional image into a Three-Dimensional image, comprising: obtaining high-frequency components and low-frequency component of the current frame, and high-frequency components of the reference frame; establishing triangular geometric models in the three directions of horizontal, vertical and diagonal, respectively; performing a motion search on the high-frequency components of the reference frame in the three directions of horizontal, vertical and diagonal, respectively, so as to obtain motion vectors in the corresponding directions, and calculating depth variations of the corresponding directions according to the motion vectors in the corresponding directions; performing an interpolating operation on the triangular geometric models in the three directions of horizontal, vertical and diagonal, respectively, according to the depth variations in the corresponding directions, so as to obtain high-frequency depth graphs in the corresponding directions; and completing a filtering reconstruction by performing an inversion wavelet transform on the high-frequency depth graphs in the three directions of horizontal, vertical and diagonal and the low-frequency component of the current frame, respectively, so as to construct a three-dimensional video image. With the present disclosure, a conversion to the 3D image from the 2D image may be realized.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is based on International Application No.PCT/CN2013/080956 filed on Aug. 7, 2013, which claims priority toChinese National Application No. 201310105607.1 filed on Mar. 28, 2013.The entire contents of each and every foregoing application areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a technique of image conversion, andparticularly to a method and an apparatus for converting aTwo-Dimensional (2D) image into a Three-Dimensional (3D) image.

BACKGROUND

A Two-Dimensional technique, also referred to as a 2D technique, belongsto one of plane techniques. Content in one plane has two dimensionswhich only indicate two directions of up-down and left-right and failsto comprise any information on a direction of forward-backward.

A Three-Dimensional technique is also referred to as a 3-D technique.The three dimensions refer to a space system composed by adding adirectional vector to the two dimensions in one plane, and arerepresented as three axes in a coordinate system: a X-axis, a Y-axis anda Z-axis, where the X-axis represents a left-right space, the Y-axisrepresents a up-down space, and the Z-axis represents a forward-backwardspace, so that a visual stereogram effect is formed.

Recently the Three-Dimensional technique has been developed rapidly, andmultiple entities in the industry try to draft standards forThree-Dimensional TV content, and encoding and transmission forThree-Dimensional TV content, however a projection apparatus and a videocamera for the Three-Dimensional images are high-cost and are notpopular. Currently the development of the Two-Dimensional image is verymature, and the Two-Dimensional technique has a perfect and low-costapplication system.

If the Two-Dimensional image may be converted into the Three-Dimensionalimage in real time, and projected in stereo by the projection apparatusfor the Two-Dimensional image, the above problem may be settled. Howeverthe prior art fails to propose a method for converting theTwo-Dimensional image into the Three-Dimensional image and playing theThree-Dimensional image by the projection apparatus for theTwo-Dimensional image.

SUMMARY

In view of above, a major object of the present disclosure is provided amethod and an apparatus for converting a Two-Dimensional (2D) image intoa Three-Dimensional (3D) image, in order to realize a conversion to the3D image from the 2D image.

In order to achieve the above object, the present disclosure providessolutions as follows.

In the present disclosure, there is provided a method for converting aTwo-Dimensional image into a Three-Dimensional image, comprising:

in a step A, performing a lifting wavelet transform on a current frameand a reference frame of a two-dimensional video image signal,respectively, to obtain high-frequency components and low-frequencycomponent of the current frame, and high-frequency components of thereference frame, the high-frequency components comprise high-frequencycomponents in three directions of horizontal, vertical and diagonal;

in a step B, establishing triangular geometric models in the threedirections of horizontal, vertical and diagonal, respectively, accordingto the high-frequency components of the current frame;

in a step C, performing a motion search on the high-frequency componentsof the reference frame in the three directions of horizontal, verticaland diagonal, respectively, by using the triangular geometric models ofthe current frame, so as to obtain motion vectors in the correspondingdirections, and calculating depth variations in the correspondingdirections according to the motion vectors in the correspondingdirections;

in a step D, performing an interpolating operation on the triangulargeometric models in the three directions of horizontal, vertical anddiagonal, respectively, according to the depth variations in thecorresponding directions, so as to obtain high-frequency depth graphs inthe corresponding directions; and

in a step E, completing a filtering reconstruction by performing aninversion wavelet transform on the high-frequency depth graphs in thethree directions of horizontal, vertical and diagonal and thelow-frequency component of the current frame, respectively, so as toconstruct a three-dimensional video image.

Preferably, the step A comprises:

setting an ith data frame x^(i)(n) of the two-dimensional video imagesignal as the current frame or the reference frame;

decomposing the x^(i)(n) as an odd sequence x_(o) ^(i)(n)=x(2n+1) and aneven sequence x_(e) ^(i)(n)=x(2n);

predicting the odd sequence using the even sequence x_(e) ^(i)(n)=x(2n)by a correlation between the odd sequence and the even sequence toobtain a prediction value

${\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}},$then subtracting the prediction value from the odd sequence x_(o)^(i)(n)=x(2n+1) to obtain the odd sequence X_(o) ^(i)(n) of the ith dataframe as

${{X_{o}^{i}(n)} = {{x_{o}^{i}(n)} - {\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}}}},$where the p^(i) is a prediction operator for predicting the odd sequenceusing the even sequence of the ith data frame, and the k is a scalingcoefficient;

filtering the predicted odd sequence X_(o) ^(i)(n), and subtracting thefiltering result from the even sequence x_(e) ^(i)(n)=x(2n) so as toobtain the even sequence X_(e) ^(i)(n) of the ith data frame as

${{X_{e}^{i}(n)} = {{x_{e}^{i}(n)} - {\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}}}},$where

$\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}$is the filtering result, and the U^(i)(k) is a updating operator;

multiplying the X_(e) ^(i)(n) with a scaling coefficient 1/k andmultiplying the X_(o) ^(i)(n) with the scaling coefficient k to obtainapproximate details of the data frame x^(i)(n) as x_(low) ^(i)(n)=X_(e)^(i)(n)/k and x_(high) ^(i)(n)=X_(o) ^(i)(n)·k, where the x_(low)^(i)(n) is the low-frequency component of the ith data frame, while thex_(high) ^(i)(n) is the high-frequency component of the ith data frame;and

filtering the x_(high) ^(i)(n) in the three directions of horizontal,vertical and diagonal, respectively, to obtain the high-frequencycomponents of the ith data frame in the three directions.

Preferably, the step B comprises:

setting vertex coordinate of each of three vertexes of each of thetriangular geometric models as (x, y, z), where the x is a coordinate inthe horizontal direction, the y is a coordinate in the verticaldirection, and the z is a coordinate in the diagonal direction;

establishing the triangular geometric model in the horizontal directionaccording to changes in the z coordinate in a situation that the ycoordinate is unchanged while the x coordinate increases;

establishing the triangular geometric model in the vertical directionaccording to changes in the z coordinate in a situation that the xcoordinate is unchanged while the y coordinate increases; and

establishing the triangular geometric model in the diagonal directionaccording to changes in the z coordinate in a situation that both the ycoordinate and the x coordinate change.

Preferably, the step C comprises:

calculating a motion vector in the horizontal direction asMV=W_(p)√{square root over (MV_(x) ²+MV_(z) ²)};

calculating a motion vector in the vertical direction asMV=W_(p)√{square root over (MV_(y) ²+MV_(z) ²)};

calculating a motion vector in the diagonal direction asMV=W_(p)√{square root over (MV_(x) ²+MV_(y) ²+MV_(z) ²)};

where the MV_(x) is difference between the x coordinates in the currentframe and the reference frame, the MV_(y) is difference between the ycoordinates in the current frame and the reference frame, the MV_(z) isdifference between the z coordinates in the current frame and thereference frame, and the W_(p) is a constant;

calculating a depth variation in each of the three directions by anequation

${{d(z)} = \frac{255 \times \left( {{MV} - {MV}_{\min}} \right)}{{MV}_{\max} - {MV}_{\min}}},$where the MV_(min) is a minimum value of the motion vectors in thecorresponding direction, and the MV_(max) is a maximum value of themotion vectors in the corresponding direction.

Preferably, the step D comprises: interpolating the depth value z of thetriangular geometric model in each of the three directions with aninterpolation value which is ½ of the depth variation d(Z) in thecorresponding direction to get z′=z+d(Z)/2; and acquiring thehigh-frequency component of the z′ in the corresponding direction as thehigh-frequency depth graph in the corresponding direction.

In the present disclosure, there is further provided an apparatus forconverting a Two-Dimensional image into a Three-Dimensional image,comprising:

a lifting wavelet transforming module for performing a lifting wavelettransform on a current frame and a reference frame of a two-dimensionalvideo image signal, respectively, to obtain high-frequency componentsand low-frequency component of the current frame, and high-frequencycomponents of the reference frame, the high-frequency componentscomprise high-frequency components in three directions of horizontal,vertical and diagonal;

a geometric model establishing module for establishing triangulargeometric models in the three directions of horizontal, vertical anddiagonal, respectively, according to the high-frequency components ofthe current frame;

a motion searching module for performing a motion search on thehigh-frequency components of the reference frame in the three directionsof horizontal, vertical and diagonal, respectively, by using thetriangular geometric models of the current frame, so as to obtain motionvectors in the corresponding directions, and calculating depthvariations in the corresponding directions according to the motionvectors in the corresponding directions;

an interpolation operating module for performing an interpolatingoperation on the triangular geometric models in the three directions ofhorizontal, vertical and diagonal, respectively, according to the depthvariations in the corresponding directions, so as to obtainhigh-frequency depth graphs in the corresponding directions; and

a reconstruction module for completing a filtering reconstruction byperforming an inversion wavelet transform on the high-frequency depthgraphs in the three directions of horizontal, vertical and diagonal andthe low-frequency component of the current frame, respectively, so as toconstruct a three-dimensional video image.

Preferably, the lifting wavelet transforming module is further used for:setting an ith data frame x^(i)(n) of the two-dimensional video imagesignal as the current frame or the reference frame;

decomposing the x^(i)(n) as an odd sequence x_(o) ^(i)(n)=x(2n+1) and aneven sequence x_(e) ^(i)(n)=x(2n);

predicting the odd sequence using the even sequence x_(e) ^(i)(n)=x(2n)by a correlation between the odd sequence and the even sequence toobtain a prediction value

${\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}},$then subtracting the prediction value from the odd sequence x_(o)^(i)(n)=x(2n+1) to obtain the odd sequence X_(o) ^(i)(n) of the ith dataframe as

${{X_{o}^{i}(n)} = {{x_{o}^{i}(n)} - {\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}}}},$where the p^(i) is a prediction operator for predicting the odd sequenceusing the even sequence of the ith data frame, and the k is a scalingcoefficient;

filtering the predicted odd sequence X_(o) ^(i)(n), and subtracting thefiltering result from the even sequence x_(e) ^(i)(n)=x(2n) so as toobtain the even sequence X_(e) ^(i)(n) of the ith data frame as

${{X_{e}^{i}(n)} = {{x_{e}^{i}(n)} - {\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}}}},$where

$\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}$is the filtering result, and the U^(i)(k) is a updating operator;

multiplying the X_(e) ^(i)(n) with a scaling coefficient 1/k andmultiplying the X_(o) ^(i)(n) with the scaling coefficient k, so as toobtain approximate details of the data frame x^(i)(n) as x_(low)^(i)(n)=X_(e) ^(i)(n)/k and x_(high) ^(i)(n)=X_(o) ^(i)(n)·k, where thex_(low) ^(i)(n) is the low-frequency component of the ith data frame,while the x_(high) ^(i)(n) is the high-frequency component of the ithdata frame; and

filtering the x_(high) ^(i)(n) in the three directions of horizontal,vertical and diagonal, respectively, so as to obtain the high-frequencycomponents of the ith data frame in the three directions.

Preferably, the geometric model establishing module is further used for:setting vertex coordinate of each of three vertexes of each of thetriangular geometric models as (x, y, z), where the x is a coordinate inthe horizontal direction, the y is a coordinate in the verticaldirection, and the z is a coordinate in the diagonal direction;establishing the triangular geometric model in the horizontal directionaccording to changes in the z coordinate in a situation that the ycoordinate is unchanged while the x coordinate increases; establishingthe triangular geometric model in the vertical direction according tochanges in the z coordinate in a situation that the x coordinate isunchanged while the y coordinate increases; and establishing thetriangular geometric model in the diagonal direction according tochanges in the z coordinate in a situation that both the y coordinateand the x coordinate change.

Preferably, the motion searching module is further used for: calculatinga motion vector in the horizontal direction as MV=W_(p)√{square rootover (MV_(x) ²+MV_(z) ²)}; calculating a motion vector in the verticaldirection as MV=W_(p)√{square root over (MV_(y) ²+MV_(z) ²)};calculating a motion vector in the diagonal direction asMV=W_(p)√{square root over (MV_(x) ²+MV_(y) ²+MV_(z) ²)}; where theMV_(x) is difference between the x coordinates in the current frame andthe reference frame, the MV_(y) is difference between the y coordinatesin the current frame and the reference frame, the MV_(z) is differencebetween the z coordinates in the current frame and the reference frame,and the W_(p) is a constant; calculating a depth variation in each ofthe three directions by an equation

${{d(z)} = \frac{255 \times \left( {{MV} - {MV}_{\min}} \right)}{{MV}_{\max} - {MV}_{\min}}},$where the MV_(min) is a minimum value of the motion vectors in thecorresponding direction, and the MV_(max) is a maximum value of themotion vectors in the corresponding direction.

Preferably, the interpolation operating module is further used for:interpolating the depth value z of the triangular geometric model ineach of the three directions with an interpolation value which is ½ ofthe depth variation d(Z) in the corresponding direction to getz′=z+d(Z)/2; and acquiring the high-frequency component of the z′ in thecorresponding direction as the high-frequency depth graph in thecorresponding direction.

The method and the apparatus for converting the Two-Dimensional imageinto the Three-Dimensional image perform the lifting wavelet transformon the current frame and the reference frame of the two-dimensionalvideo image signal, respectively, to obtain the high-frequencycomponents and the low-frequency component of the current frame, and thehigh-frequency components of the reference frame, the high-frequencycomponents of the current frame and the reference frame comprise thehigh-frequency components in three directions of horizontal, verticaland diagonal; establish the corresponding triangular geometric modelsaccording to the high-frequency components of the current frame in thethree directions; perform the motion search on the high-frequencycomponents of the reference frame in the three directions by using thetriangular geometric models of the current frame in the correspondingdirections, calculate the motion vectors in the correspondingdirections, and calculate the depth variations of the correspondingdirections according to the motion vectors in the correspondingdirections; perform the interpolation on the triangular geometric modelsin the corresponding directions according to the depth variations in thecorresponding directions to construct corresponding color spaces andcorresponding depth spaces, so as to obtain the high-frequency depthgraphs; and complete a filtering reconstruction by performing theinversion wavelet transform on the high-frequency depth graphs and thelow-frequency component of the current frame, so as to construct thethree-dimensional video image. Thus a conversion to thethree-dimensional image from the two-dimensional image is achieved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary view illustrating a process for converting aTwo-Dimensional image into a Three-Dimensional image according toembodiments of the present disclosure;

FIG. 2 is an exemplary view illustrating a process for performing alifting wavelet transform on signals according to the embodiments of thepresent disclosure;

FIG. 3 is an exemplary view illustrating the establishment of triangulargeometric models according to the embodiments of the present disclosure;

FIG. 4 is an exemplary view illustrating the establishment of thetriangular geometric models when a three-level lifting wavelet transformis utilized according to the embodiments of the present disclosure; and

FIG. 5 is a block diagram illustrating an apparatus for converting aTwo-Dimensional image into a Three-Dimensional image according toembodiments of the present disclosure.

DETAILED DESCRIPTION

A process for converting a Two-Dimensional (2D) image into aThree-Dimensional (3D) image according to embodiments of the presentdisclosure is illustrated in FIG. 1.

In a step 1, a lifting wavelet transform is performed on a current frameand a reference frame of a two-dimensional video image signal,respectively, to obtain high-frequency components and low-frequencycomponent of the current frame, and high-frequency components of thereference frame.

The high-frequency components of the current frame and thehigh-frequency components of the reference frame comprise high-frequencycomponents in three directions of horizontal, vertical and diagonal,respectively.

The reference frame refers to a frame having a strong correlation withthe current frame in temporal and spatial. Preferably, in theembodiments of the present disclosure, a frame adjacent to the currentframe in temporal may be selected as the reference frame in order toreduce calculations for the correlation. There are two frames adjacentto the current frame in temporal, and in the embodiments of the presentdisclosure, preferably, a frame next to the current frame, namely a nextframe, may be selected as the reference frame.

A process for performing the lifting wavelet transform on the data frame(the current frame or the reference frame) is as illustrated in FIG. 2.

In a decomposing process: an ith data frame x^(i)(n) is decomposed as anodd sequence x_(o) ^(i)(n)=x(2n+1) and an even sequence x_(e)^(i)(n)=x(2n).

In a predicting process: the odd sequence is predicted using the evensequence x_(e) ^(i)(n)=x(2n) by a correlation between the odd sequenceand the even sequence to obtain a prediction value

${\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}},$then the prediction value is subtracted from the odd sequence x_(o)^(i)(n)=x(2n+1) to obtain the odd sequence X_(o) ^(i)(n) of the ith dataframe as

${{X_{o}^{i}(n)} = {{x_{o}^{i}(n)} - {\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}}}},$where the p^(i) is a prediction operator for predicting the odd sequenceusing the even sequence of the ith data frame, and the k is a scalingcoefficient.

In an updating process: the predicted odd sequence X_(o) ^(i)(n) isfiltered, and the filtering result is subtracted from the even sequencex_(e) ^(i)(n)=x(2n) so as to obtain the even sequence X_(e) ^(i)(n) ofthe ith data frame as

${{X_{e}^{i}(n)} = {{x_{e}^{i}(n)} - {\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}}}},$where

$\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}$is the filtering result of the X_(o) ^(i)(n), and the U^(i)(k) is aupdating operator.

At last, the X_(e) ^(i)(n) is multiplied with a scaling coefficient 1/kand the X_(o) ^(i)(n) is multiplied with the scaling coefficient k, soas to obtain approximate details of the data frame x^(i)(n) as x_(low)^(i)(n)=X_(e) ^(i)(n)/k and x_(high) ^(i)(n)=X_(o) ^(i)(n)·k, where thex_(low) ^(i)(n) is the low-frequency component of the ith data frame,while the x_(high) ^(i)(n) is the high-frequency component of the ithdata frame.

In an example, the x_(high) ^(i)(n) is flittered in the three directionsof horizontal, vertical and diagonal, respectively, so that thehigh-frequency components corresponding to the three directions ofhorizontal, vertical and diagonal are obtained.

In a step 2, triangular geometric models in the three directions ofhorizontal, vertical and diagonal are established, respectively,according to the high-frequency components of the current frame in thecorresponding directions.

A triangle is a simplest planar polygon, variations of the colors (r, g,b) and the depth (z) of the respective points inside a triangle within ascreen space are linear in the direction of scan lines, that is, valuesfor the colors and depth of the adjacent pixel points only differ afixed constant. Also, the triangle is simpler than other polygons, andother polygons may be divided into triangles. Thus the triangle model isselected as the geometric model.

After performing the lifting wavelet transform, the triangular geometricmodels of the high-frequency components may be established as follows.

Vertex coordinate of each of three vertexes of each of the triangulargeometric models is set as (x, y, z), where the x is a coordinate in thehorizontal direction, the y is a coordinate in the vertical direction,and the z is a coordinate in the diagonal direction (representing thedepth). Then the triangular geometric model may be defined by the vertexcoordinate (x, y, z) and vertex colors (r, g, b) of each of its threevertexes, and z and the color r, g, b are linear in the direction ofscan lines.

As illustrated in FIG. 3, for the horizontal direction, the triangulargeometric model in the horizontal direction is established according tochanges in the z coordinate in a situation that the y coordinate isunchanged while the x coordinate increases; for the vertical direction,the triangular geometric model in the vertical direction is establishedaccording to changes in the z coordinate in a situation that the xcoordinate is unchanged while the y coordinate increases; and for thediagonal direction, given a 45° diagonal direction, the triangulargeometric model in the diagonal direction is established according tochanges in the z coordinate in a situation that both the y coordinateand the x coordinate change.

FIG. 4 illustrates triangular geometric models established in the threedirections of horizontal, vertical and diagonal, respectively, by takinga three-level lifting wavelet transform as an example. The so-calledn-level lifting wavelet transform refers to that: a one-level means toperform a filtering on an original image once, while a n-level means toperform the filtering on the high-frequency components filtered in the(n−1)-level continually.

In FIG. 4, HL1, LH1 and HH1 denote the high-frequency components in thehorizontal direction, the vertical direction and the diagonal direction,respectively, subjected to the one-level lifting wavelet transform; HL2,LH2 and HH2 denote the high-frequency components in the horizontaldirection, the vertical direction and the diagonal direction,respectively, subjected to the two-level lifting wavelet transform; HL3,LH3 and HH3 denote the high-frequency components in the horizontaldirection, the vertical direction and the diagonal direction,respectively, subjected to the three-level lifting wavelet transform. Agraph formed by little corresponding grids in FIG. 4 represents a sizeof the image, and it may be seen from the figure that the imagesubjected to the lifting wavelet transform per level is scaled by ascaling parameter K.

In a step 3, a motion search is performed on the high-frequencycomponents of the reference frame in the three directions of horizontal,vertical and diagonal, respectively, by using the triangular geometricmodels of the current frame, so as to obtain motion vectors in thecorresponding directions, and depth variations in the correspondingdirections are calculated according to the motion vectors in thecorresponding direction.

The triangular geometric models in the three directions of horizontal,vertical and diagonal of the current frame are established respectivelythrough the step 2. The motion search is performed on the high-frequencycomponent of the reference frame subjected to the lifting wavelettransform in a direction by making the established triangular geometricmodel in the direction as a module.

For example, the motion search is performed on the high-frequencycomponent in the horizontal direction of the reference frame by usingthe triangular geometric model in the horizontal direction of thecurrent frame; the motion search is performed on the high-frequencycomponent in the vertical direction of the reference frame by using thetriangular geometric model in the vertical direction of the currentframe; and the motion search is performed on the high-frequencycomponent in the diagonal direction of the reference frame by using thetriangular geometric model in the diagonal direction of the currentframe.

Motion vectors may be calculated with an Equation (1):MV=W _(p)√{square root over (MV _(x) ² +MV _(y) ² +MV _(z) ²)}.  (1)

where the MV_(x) is difference between the x coordinates in the currentframe and the reference frame, the MV_(y) is difference between the ycoordinates in the current frame and the reference frame, the MV_(z) isdifference between the z coordinates in the current frame and thereference frame, and the W_(p) is a constant and may be set as 1.

The motion vector in the x direction, namely the horizontal direction,is a vector for the depth z being changing with the changes in the xcoordinate. When the motion vector in the horizontal direction iscalculated, lets MV_(Y) ²=0, then the Equation (1) is evolved asMV=W_(p)√{square root over (MV_(x) ²+MV_(z) ²)}.

The motion vector in the y direction, namely the vertical direction, isa vector for the depth z being changing with the changes in the ycoordinate. When the motion vector in the horizontal direction iscalculated, lets MV_(x) ²=0, then the Equation (1) is evolved asMV=W_(p)√{square root over (MV_(y) ²+MV_(z) ²)}.

The motion vector in the z direction, namely the diagonal direction, isa vector for the depth z being changing with the changes in the xcoordinate and the y coordinate, and may be calculated with the Equation(1).

The motion vector in the horizontal direction, the motion vector in thevertical direction and the motion vector in the diagonal direction maybe obtained with the above Equation (1), respectively.

Depth variations may be obtained by an equation d(z) as follows:

$\begin{matrix}{{{d(z)} = \frac{255 \times \left( {{MV} - {MV}_{\min}} \right)}{{MV}_{\max} - {MV}_{\min}}},} & (2)\end{matrix}$

As calculating the depth variation in a certain direction, a maximumvalue MV_(max) and a minimum value MV_(min) of the motion vectors in thedirection are required to be calculated firstly. Both of the maximumvalue and the minimum value of the motion vectors are calculated withthe Equation (1), and only their corresponding reference frames aredifferent.

The depth variations in the three directions of horizontal, vertical anddiagonal may be obtained finally with the Equation (2).

In a step 4, an interpolating operation is performed on the triangulargeometric models in the three directions of horizontal, vertical anddiagonal, respectively, according to the depth variations in thecorresponding direction, so as to obtain high-frequency depth graphs inthe corresponding directions.

In order to express differences of the depth, the interpolatingoperation is needed to be performed on the triangular geometric modelsin a direction with the depth variations in the corresponding direction.In particular, the depth value z (the depth value z is an image depthfor each pixel and is known, which refers to a gray scale correspondingto the current pixel) of the triangular geometric model in thecorresponding direction is interpolated with an interpolation valuewhich is ½ of the depth variation d(Z) in the corresponding direction toget z′=z+d(Z)/2, a corresponding color space and a corresponding depthspace are formed finally, and a high-frequency depth graph is obtained.The high-frequency depth graph is the high-frequency component of the z′in the corresponding direction.

In a step 5, a filtering reconstruction is completed by performing aninversion wavelet transform on the high-frequency depth graphs in thethree directions of horizontal, vertical and diagonal and thelow-frequency component of the current frame, respectively, and athree-dimensional video image is constructed.

This step is an inversion transform of the lifting wavelet transform(that is, a new image is obtained by performing the inversion transformon the wavelet sub-band processed), the inversion transform process aimsto perform reconstruction by using the obtained high-frequency depthgraphs and the low-frequency component.

In order to realize the above method, the present disclosure furtherprovides an apparatus for converting a Two-Dimensional image into aThree-Dimensional image, as illustrated in FIG. 5, comprising:

a lifting wavelet transforming module 10 for performing a liftingwavelet transform on a current frame and a reference frame of atwo-dimensional video image signal, respectively, to obtainhigh-frequency components and low-frequency component of the currentframe, and high-frequency components of the reference frame, thehigh-frequency components comprise high-frequency components in threedirections of horizontal, vertical and diagonal;

a geometric model establishing module 20 for establishing triangulargeometric models in the three directions of horizontal, vertical anddiagonal, respectively, according to the high-frequency components ofthe current frame;

a motion searching module 30 for performing a motion search on thehigh-frequency components of the reference frame in the three directionsof horizontal, vertical and diagonal, respectively, by using thetriangular geometric models of the current frame, so as to obtain motionvectors in the corresponding directions, and calculating depthvariations in the corresponding directions according to the motionvectors in the corresponding directions;

an interpolation operating module 40 for performing an interpolatingoperation on the triangular geometric models in the three directions ofhorizontal, vertical and diagonal, respectively, according to the depthvariations in the corresponding directions, so as to obtainhigh-frequency depth graphs in the corresponding directions; and

a reconstruction module 50 for completing a filtering reconstruction byperforming an inversion wavelet transform on the high-frequency depthgraphs in the three directions of horizontal, vertical and diagonal andthe low-frequency component of the current frame, respectively, so as toconstruct a three-dimensional video image.

The lifting wavelet transforming module 10 is further used for: settingan ith data frame x^(i)(n) of the two-dimensional video image signal asthe current frame or the reference frame; decomposing the x^(i)(n) as anodd sequence x_(o) ^(i)(n)=x(2n+1) and an even sequence x_(e)^(i)(n)=x(2n); predicting the odd sequence using the even sequence x_(e)^(i)(n)=x(2n) by a correlation between the odd sequence and the evensequence to obtain a prediction value

${\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}},$then subtracting the prediction value from the odd sequence x_(o)^(i)(n)=x(2n+1) to obtain the odd sequence X_(o) ^(i)(n) of the ith dataframe as

${{X_{o}^{i}(n)} = {{x_{o}^{i}(n)} - {\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}}}},$where the p^(i) is a prediction operator for predicting the odd sequenceusing the even sequence of the ith data frame, and the k is a scalingcoefficient; filtering the predicted odd sequence X_(o) ^(i)(n), andsubtracting the filtering result from the even sequence x_(e)^(i)(n)=x(2n) so as to obtain the even sequence X_(e) ^(i)(n) of the ithdata frame as

${{X_{e}^{i}(n)} = {{x_{e}^{i}(n)} - {\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}}}},$where

$\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}$is the filtering result, and the U^(i)(k) is a updating operator.

The lifting wavelet transforming module 10 is further used formultiplying the X_(e) ^(i)(n) with a scaling coefficient 1/k andmultiplying the X_(o) ^(i)(n) with the scaling coefficient k, so as toobtain approximate details of the data frame x^(i)(n) as x_(low)^(i)(n)=X_(e) ^(i)(n)/k and x_(high) ^(i)(n)=X_(o) ^(i)(n)·k, where thex_(low) ^(i)(n) is the low-frequency component of the ith data frame,while the x_(high) ^(i)(n) is the high-frequency component of the ithdata frame; and filtering the x_(high) ^(i)(n) in the three directionsof horizontal, vertical and diagonal, respectively, so as to obtain thehigh-frequency components of the ith data frame in the three directions.

The geometric model establishing module 20 is further used for: settingvertex coordinate of each of three vertexes of each of the triangulargeometric models as (x, y, z), where the x is a coordinate in thehorizontal direction, the y is a coordinate in the vertical direction,and the z is a coordinate in the diagonal direction; establishing thetriangular geometric model in the horizontal direction according tochanges in the z coordinate in a situation that the y coordinate isunchanged while the x coordinate increases; establishing the triangulargeometric model in the vertical direction according to changes in the zcoordinate in a situation that the x coordinate is unchanged while the ycoordinate increases; and establishing the triangular geometric model inthe diagonal direction according to changes in the z coordinate in asituation that both the y coordinate and the x coordinate change.

The motion searching module 30 is further used for: calculating a motionvector in the horizontal direction as MV=W_(p)√{square root over (MV_(x)²+MV_(z) ²)}; calculating a motion vector in the vertical direction asMV=W_(p)√{square root over (MV_(y) ²+MV_(z) ²)}; calculating a motionvector in the diagonal direction as MV=W_(p)√{square root over (MV_(x)²+MV_(y) ²+MV_(z) ²)}; where the MV_(x) is difference between the xcoordinates in the current frame and the reference frame, the MV_(y) isdifference between the y coordinates in the current frame and thereference frame, the MV_(z) is difference between the z coordinates inthe current frame and the reference frame, and the W_(p), is a constant;calculating a depth variation in each of the three directions by anequation

${{d(z)} = \frac{255 \times \left( {{MV} - {MV}_{m\; i\; n}} \right)}{{MV}_{{ma}\; x} - {MV}_{m\; i\; n}}},$where the MV_(min) is a minimum value of the motion vectors in thecorresponding direction, and the MV_(max) is a maximum value of themotion vectors in the corresponding direction.

The interpolation operating module 40 is further used for: interpolatingthe depth value z of the triangular geometric model in each of the threedirections with an interpolation value which is ½ of the depth variationd(Z) in the corresponding direction to get z′=z+d(Z)/2; and acquiringthe high-frequency component of the z′ in the corresponding direction asthe high-frequency depth graph in the corresponding direction.

The above descriptions are only preferred embodiments of the presentdisclosure and are not intend to limit the scope sought for protectionof the present disclosure.

What is claimed is:
 1. A method for converting a Two-Dimensional videoimage into a Three-Dimensional video image, comprising: in a step A,performing, by a processing unit, a lifting wavelet transform on acurrent frame and a reference frame of a two-dimensional video imagesignal, respectively, to obtain high-frequency components andlow-frequency component of the current frame, and high-frequencycomponents of the reference frame, the high-frequency componentscomprise high-frequency components in three directions of horizontal,vertical and diagonal; in a step B, establishing, by the processingunit, triangular geometric models in the three directions of horizontal,vertical and diagonal, respectively, according to the high-frequencycomponents of the current frame; in a step C, performing, by theprocessing unit, a motion search on the high-frequency components of thereference frame in the three directions of horizontal, vertical anddiagonal, respectively, by using the triangular geometric models of thecurrent frame, so as to obtain motion vectors in the correspondingdirections, and calculating depth variations in the correspondingdirections according to the motion vectors in the correspondingdirections; in a step D, performing, by the processing unit, aninterpolating operation on the triangular geometric models in the threedirections of horizontal, vertical and diagonal, respectively, accordingto the depth variations in the corresponding directions, so as to obtainhigh-frequency depth graphs in the corresponding directions; and in astep E, completing, by the processing unit, a filtering reconstructionby performing an inversion wavelet transform on the high-frequency depthgraphs in the three directions of horizontal, vertical and diagonal andthe low-frequency component of the current frame, respectively, so as toconstruct a three-dimensional video image.
 2. The method for convertinga Two-Dimensional video image into a Three-Dimensional video image ofclaim 1, wherein the step A comprises: setting an ith data framex^(i)(n) of the two-dimensional video image signal as the current frameor the reference frame; decomposing the x^(i)(n) as an odd sequencex_(o) ^(i)(n)=x(2n+1) and an even sequence x_(e) ^(i)(n)=x(2n);predicting the odd sequence using the even sequence x_(e) ^(i)(n)=x(2n)by a correlation between the odd sequence and the even sequence toobtain a prediction value${\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}},$ thensubtracting the prediction value from the odd sequence x_(o)^(i)(n)=x(2n+1) to obtain the odd sequence X_(o) ^(i)(n) of the ith dataframe as${{X_{o}^{i}(n)} = {{x_{o}^{i}(n)} - {\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}}}},$wherein the p^(i) is a prediction operator for predicting the oddsequence using the even sequence of the ith data frame, and the k is ascaling coefficient; filtering the predicted odd sequence X_(o) ^(i)(n),and subtracting the filtering result from the even sequence x_(e)^(i)(n)=x(2n) so as to obtain the even sequence X_(e) ^(i)(n) of the ithdata frame as${{X_{e}^{i}(n)} = {{x_{e}^{i}(n)} - {\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}}}},$wherein $\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}$ is thefiltering result, and the U^(i)(k) is a updating operator; multiplyingthe X_(e) ^(i)(n) with a scaling coefficient 1/k and multiplying theX_(o) ^(i)(n) with the scaling coefficient k, to obtain approximatedetails of the data frame x^(i)(n) as x_(low) ^(i)(n)=X_(e) ^(i)(n)/kand x_(high) ^(i)(n)=X_(o) ^(i)(n)·k, wherein the x_(low) ^(i)(n) is thelow-frequency component of the ith data frame, while the x_(high)^(i)(n) is the high-frequency component of the ith data frame; andfiltering the x_(high) ^(i)(n) in the three directions of horizontal,vertical and diagonal, respectively, to obtain the high-frequencycomponents of the ith data frame in the three directions.
 3. The methodfor converting a Two-Dimensional video image into a Three-Dimensionalvideo image of claim 2, wherein the step B comprises: setting vertexcoordinate of each of three vertexes of each of the triangular geometricmodels as (x, y, z), wherein the x is a coordinate in the horizontaldirection, the y is a coordinate in the vertical direction, and the z isa coordinate in the diagonal direction; establishing the triangulargeometric model in the horizontal direction according to changes in thez coordinate in a situation that the y coordinate is unchanged while thex coordinate increases; establishing the triangular geometric model inthe vertical direction according to changes in the z coordinate in asituation that the x coordinate is unchanged while the y coordinateincreases; and establishing the triangular geometric model in thediagonal direction according to changes in the z coordinate in asituation that both the y coordinate and the x coordinate change.
 4. Themethod for converting a Two-Dimensional video image into aThree-Dimensional video image of claim 3, wherein the step C comprises:calculating a motion vector in the horizontal direction asMV=W_(p)√{square root over (MV_(x) ²+MV_(z) ²)}; calculating a motionvector in the vertical direction as MV=W_(p)√{square root over (MV_(y)²+MV_(z) ²)}; calculating a motion vector in the diagonal direction asMV=W_(p)√{square root over (MV_(x) ²+MV_(y) ²+MV_(z) ²)}; wherein theMV_(x) is difference between the x coordinates in the current frame andthe reference frame, the MV_(y) is difference between the y coordinatesin the current frame and the reference frame, the MV_(z) is differencebetween the z coordinates in the current frame and the reference frame,and the W_(p) is a constant; calculating a depth variation in each ofthe three directions by an equation${{d(z)} = \frac{255 \times \left( {{MV} - {MV}_{m\; i\; n}} \right)}{{MV}_{{ma}\; x} - {MV}_{m\; i\; n}}},$wherein the MV_(min) is a minimum value of the motion vectors in thecorresponding direction, and the MV_(max) is a maximum value of themotion vectors in the corresponding direction.
 5. The method forconverting a Two-Dimensional video image into a Three-Dimensional videoimage of claim 4, wherein the step D comprises: interpolating the depthvalue z of the triangular geometric model in each of the threedirections with an interpolation value which is ½ of the depth variationd(Z) in the corresponding direction to get z′=z+d(Z)/2; and acquiringthe high-frequency component of the z′ in the corresponding direction asthe high-frequency depth graph in the corresponding direction.
 6. Anapparatus for converting a Two-Dimensional video image into aThree-Dimensional video image, comprising: a processing unit; a storageunit, a lifting wavelet transforming module resident on the storage unitfor performing a lifting wavelet transform on a current frame and areference frame of a two-dimensional video image signal, respectively,to obtain high-frequency components and low-frequency component of thecurrent frame, and high-frequency components of the reference frame, thehigh-frequency components comprise high-frequency components in threedirections of horizontal, vertical and diagonal; a geometric modelestablishing module resident on the storage unit for establishingtriangular geometric models in the three directions of horizontal,vertical and diagonal, respectively, according to the high-frequencycomponents of the current frame; a motion searching module resident onthe storage unit for performing a motion search on the high-frequencycomponents of the reference frame in the three directions of horizontal,vertical and diagonal, respectively, by using the triangular geometricmodels of the current frame, so as to obtain motion vectors in thecorresponding directions, and calculating depth variations in thecorresponding directions according to the motion vectors in thecorresponding directions; an interpolation operating module resident onthe storage unit for performing an interpolating operation on thetriangular geometric models in the three directions of horizontal,vertical and diagonal, respectively, according to the depth variationsin the corresponding directions, so as to obtain high-frequency depthgraphs in the corresponding directions; and a reconstruction moduleresident on the storage unit for completing a filtering reconstructionby performing an inversion wavelet transform on the high-frequency depthgraphs in the three directions of horizontal, vertical and diagonal andthe low-frequency component of the current frame, respectively, so as toconstruct a three-dimensional video image.
 7. The apparatus forconverting a Two-Dimensional video image into a Three-Dimensional videoimage of claim 6, wherein the lifting wavelet transforming module isfurther used for: setting an ith data frame x^(i)(n) of thetwo-dimensional video image signal as the current frame or the referenceframe; decomposing the x^(i)(n) as an odd sequence x_(o) ^(i)(n)=x(2n+1)and an even sequence x_(e) ^(i)(n)=x(2n); predicting the odd sequenceusing the even sequence x_(e) ^(i)(n)=x(2n) by a correlation between theodd sequence and the even sequence to obtain a prediction value${\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}},$ thensubtracting the prediction value from the odd sequence x_(o)^(i)(n)=x(2n+1) to obtain the odd sequence X_(o) ^(i)(n) of the ith dataframe as${{X_{o}^{i}(n)} = {{x_{o}^{i}(n)} - {\sum\limits_{k}{p^{i} \cdot {x_{e}^{i}\left( {n - k} \right)}}}}},$wherein the p^(i) is a prediction operator for predicting the oddsequence using the even sequence of the ith data frame, and the k is ascaling coefficient; filtering the predicted odd sequence X_(o) ^(i)(n),and subtracting the filtering result from the even sequence x_(e)^(i)(n)=x(2n) so as to obtain the even sequence X_(e) ^(i)(n) of the ithdata frame as${{X_{e}^{i}(n)} = {{x_{e}^{i}(n)} - {\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}}}},$wherein $\sum\limits_{k}{{U^{i}(k)} \cdot {x_{o}^{i}(n)}}$ is thefiltering result, and the U^(i)(k) is a updating operator; multiplyingthe X_(e) ^(i)(n) with a scaling coefficient 1/k and multiplying theX_(o) ^(i)(n) with the scaling coefficient k, so as to obtainapproximate details of the data frame x^(i)(n) as x_(low) ^(i)(n)=X_(e)^(i)(n)/k and x_(high) ^(i)(n)=X_(o) ^(i)(n)·k, wherein the x_(low)^(i)(n) is the low-frequency component of the ith data frame, while thex_(high) ^(i)(n) is the high-frequency component of the ith data frame;and filtering the x_(high) ^(i)(n) in the three directions ofhorizontal, vertical and diagonal, respectively, so as to obtain thehigh-frequency components of the ith data frame in the three directions.8. The apparatus for converting a Two-Dimensional video image into aThree-Dimensional video image of claim 7, wherein the geometric modelestablishing module is further used for: setting vertex coordinate ofeach of three vertexes of each of the triangular geometric models as (x,y, z), wherein the x is a coordinate in the horizontal direction, the yis a coordinate in the vertical direction, and the z is a coordinate inthe diagonal direction; establishing the triangular geometric model inthe horizontal direction according to changes in the z coordinate in asituation that the y coordinate is unchanged while the x coordinateincreases; establishing the triangular geometric model in the verticaldirection according to changes in the z coordinate in a situation thatthe x coordinate is unchanged while the y coordinate increases; andestablishing the triangular geometric model in the diagonal directionaccording to changes in the z coordinate in a situation that both the ycoordinate and the x coordinate change.
 9. The apparatus for convertinga Two-Dimensional video image into a Three-Dimensional video image ofclaim 8, wherein the motion searching module is further used for:calculating a motion vector in the horizontal direction asMV=W_(p)√{square root over (MV_(x) ²+MV_(z) ²)}; calculating a motionvector in the vertical direction as MV=W_(p)√{square root over (MV_(Y)²+MV_(Z) ²)}; calculating a motion vector in the diagonal direction asMV=W_(p)√{square root over (MV_(x) ²+MV_(y) ²+MV_(z) ²)}; wherein theMV_(x) is difference between the x coordinates in the current frame andthe reference frame, the MV_(y) is difference between the y coordinatesin the current frame and the reference frame, the MV_(z) is differencebetween the z coordinates in the current frame and the reference frame,and the W_(p) is a constant; calculating a depth variation in each ofthe three directions by an equation${{d(z)} = \frac{255 \times \left( {{MV} - {MV}_{m\; i\; n}} \right)}{{MV}_{{ma}\; x} - {MV}_{m\; i\; n}}},$wherein the MV_(min) is a minimum value of the motion vectors in thecorresponding direction, and the MV_(max) is a maximum value of themotion vectors in the corresponding direction.
 10. The apparatus forconverting a Two-Dimensional video image into a Three-Dimensional videoimage of claim 9, wherein the interpolation operating module is furtherused for: interpolating the depth value z of the triangular geometricmodel in each of the three directions with an interpolation value whichis ½ of the depth variation d(Z) in the corresponding direction to getz′=z+d(Z)/2; and acquiring the high-frequency component of the z′ in thecorresponding direction as the high-frequency depth graph in thecorresponding direction.